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New Robust Exponential Stability Analysis for Uncertain Neural Networks with Time-varying Delay 被引量：3

New Robust Exponential Stability Analysis for Uncertain Neural Networks with Time-varying Delay
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摘要 In this paper, the global robust exponential stability is considered for a class of neural networks with parametric uncer-tainties and time-varying delay. By using Lyapunov functional method, and by resorting to the new technique for estimating the upper bound of the derivative of the Lyapunov functional, some less conservative exponential stability criteria are derived in terms of linear matrix inequalities (LMIs). Numerical examples are presented to show the effectiveness of the proposed method. In this paper,the global robust exponential stability is considered for a class of neural networks with parametric uncer- tainties and time-varying delay.By using Lyapunov functional method,and by resorting to the new technique for estimating the upper bound of the derivative of the Lyapunov functional,some less conservative exponential stability criteria are derived in terms of linear matrix inequalities (LMIs).Numerical examples are presented to show the effectiveness of the proposed method.
作者 Yong-Gang Chen Wei-Ping Bi
机构地区 Department of Mathematics College of Mathematics and Information Science
出处 《International Journal of Automation and computing》 EI 2008年第4期395-400,共6页 国际自动化与计算杂志(英文版)
基金 Natural Science Foundation of Henan Education Department (No.2007120005).
关键词 Robust exponential stability uncertain neural networks time-varying delay Lyapunov functional method linear matrix inequalities(LMIs). Robust exponential stability uncertain neural networks time-varying delay Lyapunov functional method linear matrix inequalities (LMIs).
分类号 TP183 [自动化与计算机技术—控制理论与控制工程]
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参考文献21

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同被引文献28

  • 1Xu-Yang Lou Bao-Tong Cui.Delay-dependent Criteria for Robust Stability of Uncertain Switched Hopfield Neural Networks[J].International Journal of Automation and computing,2007,4(3):304-314. 被引量：2
  • 2Hopfield J J.Neurons with graded response have collective computational properties like those of two-state neurons[J].Proc Aead Sci USA, 1984,81:3088-3092.
  • 3Zhao Hongyong.Global asymptotic stability of Hopfield neural network involving distributed delays[J].Neural Networks, 2004, 17:47-53.
  • 4Liu Yurong, Wang Zidong, Liu Xiaohui.Asymptotic stability for neural networks with mixed time-delays: the discrete-time case[J].Neural Networks, 2009,22: 67-74.
  • 5Su Weiwei, Chen Yiming.Global asymptotic stability analysis for neutral stochastic neural networks with time-varying delays[J]. Commun Nonlinear Sci Numer Simulat.2009,14:1576-1581.
  • 6Yang Degang,Hua Chunyan.Novel delay-dependent global asymptotic stability condition of Hopfield neural networks with delays[J]. Computers and Mathematics with Applications, 2009,57:1978-1984.
  • 7Song Qiankun.Exponential stability of recurrent neural networks with both time-varying delays and general activation functions via LMI approach[J].Neurocomputing,2008,71:2823-2830.
  • 8Wu Huaiqin.Global exponential stability of Hopfield neural networks with delays and inverse Lipschitz neuron activations[J]. Nonlinear Analysis: Real World Applications, 2009 (10) : 2297-2306.
  • 9Samidurai R.Global exponential stability of neutral-type impulsive neural networks with discrete and distributed delays[J].Nonlinear Analysis:Hybrid Systems,2009(8).
  • 10Li Tao, Guo Lei, Wu Lingyao, et al.Delay-dependent robust stability criteria for delay neural networks with linear fractional uncertainties[J].Intemational Journal of Control, Automation, and Systems, 2009,7 (2) : 281-287.

引证文献3

二级引证文献8

International Journal of Automation and computing
2008年 第4期

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